Models of Neural Credit Assignment Mechanisms

Learning is a core element of the every day life. The underlying neurophysiological processes enable learning are called synaptic plasticity. The term synaptic plasticity incorporates all processes by which the communication between two neurons via the strength of their respective synapse either get enhanced or depressed. These changes can persist for short temporal scopes in the range of seconds to minutes or long durations lasting for days or even years.

The exact mechanisms of synaptic plasticity are yet to be uncovered. Even though a huge body of experimental literature exists key questions could not be answered satisfactorily: how does credit or a reward signal gets assigned to single synapses so that the neural networks performance get enhanced by the adaption of these tagged connections.

This project is going to explore possible algorithms for the credit assignment problem outlined in the paragraph above in spiking neural networks, a family of neuronal networks able to model the majority of spiking behaviors of neurons found in the brain. The learning algorithms will be constructed along existing experimental data from neuroscience to ensure biological plausibility and offering the possibility to form testable hypothesis based on the model verifiable by experiments. Therefore, the project would contribute to a better understanding of the fundamental mechanisms of learning in the brain.
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